Evidence (3566 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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Occupational reallocation occurs: declines in some routine occupations alongside growth in AI-complementary roles (e.g., AI maintenance, oversight, and creative tasks).
Administrative and household employment data analyzed with occupational breakdowns, supplemented by task-mapping methods and panel/event-study approaches documenting shifting occupational shares over time.
Lower-skill roles experience mixed outcomes: some see adverse effects from automation while others benefit where AI is complementary to their tasks.
Microdata analyses and case studies showing heterogeneous effects by task complementarity; task-based exposure measures that differentiate which low-skill tasks are automatable versus augmentable.
AI contributes to wage polarization: earnings grow at the top of the distribution and stagnate or fall for middle occupations.
Wage distribution decompositions and panel regression studies that examine percentile-level wage changes, combined with task-based exposure measures linking AI adoption to differential impacts across the wage distribution.
The employment impact of automation depends crucially on labour-market structure (formal vs informal), availability of alternative employment, and social protections.
Theoretical framing supported by secondary literature comparing institutional contexts and their mediating effects on automation outcomes; no primary causal estimates in this paper.
Standard policy responses focused on retraining and active labor-market programs are necessary but insufficient to fully offset structural job losses where K_T substitutes broadly for tasks.
Model simulations and policy experiments in the calibrated dynamic model comparing scenarios with aggressive retraining versus structural fiscal/interventionist reforms; discussion of empirical limits from case studies and historical reskilling outcomes.
Routine automation of routine drafting tasks by GLAI may reduce demand for junior drafting labor while increasing demand for skilled reviewers, auditors, and legal technologists.
Labor-market reasoning based on task automation literature and illustrative vignettes; no labor-force survey or longitudinal employment data provided.
Engagement is systematically tied to the intensive, performative labor of children (the platform rewards commodification of the child's identity and labor over traditional advertising), which challenges policy frameworks focused solely on financial trusts.
Synthesis/interpretation based on observed correlations and within-channel view premiums for performative and emotional-bait content versus lack of premium for explicit product placement; policy implication drawn by authors.
Current benchmarks for occupational AI agents are scoped primarily by economic values, telling a replacement story.
Author's characterization/literature-based claim in the paper (argumentative statement; no empirical data provided in this excerpt).
The observed wage penalty in high-exposure neighborhoods is driven by task de-skilling and intensified labor-market crowding.
Mechanism analyses linking task-level changes (de-skilling as measured by task assessments) and measures of labor-market crowding to the wage penalties observed in high-exposure neighborhoods, using the same 5 million job postings and task-aggregation approach.
Consolidation creates platform monopolies extracting value from professional labour while eliminating the expertise that creates it.
Synthesis of market concentration data and theoretical frameworks (platform capitalism) presented in the paper.
AI implementation serves vendor interests in labour cost reduction rather than improving information access.
Analytic argument supported by synthesis of vendor consolidation data, documented implementations, and theoretical analysis of vendor incentives.
Librarians bear operational accountability for systems they neither control nor can modify.
Critical qualitative synthesis including a revelatory case study of verification infrastructure failure and theoretical framing (platform capitalism, sociology of professions, critical information science).
The tech industry's discourse of exceptionalism obscures its dependence on BPOs to externalise labour costs and accountability.
Argument in paper supported by the authors' GDPR-based document findings that reveal BPO involvement and contract practices; specific linkage details not provided in the excerpt.
Institutionally, high-wage Nordic regimes paradoxically impose opportunity costs.
Comparative cross-national analysis across European welfare regimes using SHARE (2016-2021), indicating higher opportunity costs (e.g., foregone earnings) in high-wage Nordic countries.
Rigid gender dynamics trigger labor market ejection.
Analysis linking gender-role patterns among caregivers in SHARE (2016-2021) to negative employment outcomes (labor market exit/ejection) for affected individuals.
AI created challenges by reducing routine-based employment.
Authors' interpretation of the empirical findings from SEM and descriptive statistics on the survey sample (n=320); the summary states routine-based employment was reduced but no numerical estimate provided in the summary.
Unless targeted interventions occur — including inclusive education, vocational training, and labor reforms — AI may exacerbate poverty and joblessness.
Inference and policy recommendation based on the systematic review's identification of risks; presented as a conditional/forecast rather than a measured causal estimate in the summary.
Because experienced workers are aging out of the workforce, simultaneous curtailment of formative occupational layers by platforms may create a shortage of workers able to manage complex systems.
Argument combining demographic observation (aging workforce) with the paper's theoretical claim about erosion of entry-level apprenticeship layers; no empirical test or quantified projection provided.
Unstructured physical trades and high-stakes caretaking roles exhibit absolute resilience to LLM-driven automation (i.e., very low OAI), quantifying a 'Cognitive Risk Asymmetry.'
Empirical classification from computed OAIs showing low exposure for unstructured physical trades and high-stakes caretaking roles; the excerpt does not provide specific OAI values or counts.
Variance-based Human-in-the-Loop (HITL) validation with an expert panel demonstrates a profound cognitive gap: isolated algorithmic probabilities fail to encapsulate the "institutional premium" imposed by experts bounded by professional liability.
Empirical validation procedure reported: variance-based HITL validation involving an expert panel that compared algorithmic scores and expert adjustments, concluding a systematic difference attributed to institutional liability considerations. The excerpt does not give panel size or quantitative variance statistics.
Roughly half of the projected LFPR decline to 55% by 2050 is attributable to AI—equivalent to around 10 million lost jobs.
Authors' decomposition/interpretation of conditional forecast results under the rapid scenario reported in the abstract (ties LFPR decline to job-count equivalents).
That measured machine-equivalent work appeared on no financial statement, workforce report, or government statistical return.
Claim about absence of reporting for the deployment's measured work (asserted in the paper for the deployment case).
The emergence and diffusion of these technologies create an era of labor displacement.
Framed in the paper as a premise motivating policy proposals; presented as a conceptual claim rather than supported by original empirical estimates in the text provided.
The economic inevitability of technological transformation (in agentic finance) and the critical urgency of proactive intervention.
Author claim synthesizing the paper's argument and modeling results (normative conclusion based on earlier analysis and assertions, not a validated empirical finding).
Surveillance intensity is associated with hyper-vigilance (reported effect = -4.213).
One of the six propositions from the paper's trilevel framework; the abstract reports an effect value of '-4.213' associated with surveillance intensity → hyper-vigilance.
Platform workers receive 36.3% more third-party ratings than traditional workers.
Quantitative synthesis/summary reported in the paper (no primary sample size in abstract); likely aggregated from included studies.
Platform workers experience 59.6% higher digital speed determination than traditional workers.
Quantitative synthesis/summary reported in the paper (no primary sample size given in the abstract); presumably aggregated from included studies comparing platform and traditional workers.
The pre-existing AI community dissolved as the tools went mainstream, and the new vocabulary was absorbed into existing careers rather than binding a new occupation.
Interpretation of resume-data patterns: observed dispersion of previously coherent AI practitioners and spread of AI-related vocabulary into other occupational records rather than consolidation into a new occupational cluster.
Most existing candidate matching systems act as keyword filters, failing to handle skill synonyms and nonlinear careers, resulting in missed candidates and opaque match scores.
Paper's introductory assertion about limitations of most current systems. The excerpt does not cite empirical studies, statistics, or systematic reviews to substantiate this claim.
Counterfactual simulations show that modest salary increases have a smaller effect on predicted attrition than eliminating overtime (in this dataset and model).
Comparative counterfactual experiments run on the calibrated logistic model: simulations altering salary vs. altering overtime feature; reported that overtime elimination outperforms modest pay increases in retained headcount and probability reductions (exact salary-change amounts and comparative numbers not given in the summary).
In the dataset used, eliminating overtime could potentially retain about 31 employees — a larger effect than modest salary increases.
Aggregated counterfactual simulation on the IBM HR Analytics dataset: after setting overtime to zero for applicable records, the model-predicted net retained headcount ≈ 31; compared to simulations of modest salary increases which yielded smaller retained headcount (exact salary-change magnitude and headcount numbers not provided).
Eliminating overtime could lower predicted attrition probability by 17.35% for affected employees (per the model's counterfactual simulation).
Counterfactual policy simulation using the calibrated logistic model on the IBM HR Analytics dataset: set overtime feature to zero for affected employees and compute change in each employee's calibrated attrition probability; reported average reduction = 17.35%.
AI adoption is skill-biased and spatially uneven, increasing risks of labor-market exclusion among low-educated, middle-aged workers in high-AI regions.
Inference from observed negative associations between AI-rich regions and employment intention for low-educated respondents in the survey of 889; supported by region-level AI adoption proxies used in regressions.
Regional heterogeneity: eastern and northern areas with greater AI penetration intensify displacement pressure on low-skilled, pre-retirement workers.
Subsample/interaction results in the regression analysis separating regions (Beijing, Guangzhou, Lanzhou and broader eastern/northern regional classification) and linking regional AI penetration proxies to employment intention outcomes among low-skilled workers.
Low-educated workers—especially in eastern and northern regions with greater AI adoption—experience increased displacement pressure and lower employment intent.
Interaction/heterogeneity analysis from multivariate regressions on the sample of 889 respondents, using region-level AI adoption intensity (proxied by region) to identify differential associations by education level; stronger negative associations for low-educated respondents in eastern and northern areas.
Higher household economic pressure is negatively associated with willingness to remain employed pre-retirement.
Regression controls included household economic pressure measured in the cross-sectional survey (n=889); coefficient on economic pressure indicated a negative association with employment intention.
Environmental and informational externalities from AI (energy use, privacy harms, bias) justify regulatory and Pigouvian-style interventions to correct market failures.
Conceptual and policy literature reviewed, combined with empirical observations about environmental impacts and privacy/bias incidents reported in prior studies; the paper does not provide new causal estimates of externality magnitudes.
AI may alter firms' competitive dynamics by amplifying scale advantages and platform effects, making antitrust, data portability, and competition policy relevant to preserve contestability and innovation.
Synthesis of industrial organization theory and empirical observations of platform markets and data-driven firms cited in the literature review; no primary empirical study included in this paper.
The under‑use of external text sources in the reviewed literature may be due to privacy, legal/regulatory uncertainty, or integration costs.
Authors' interpretation linking observed low coverage of external text sources (social media, news, reviews) in the 109 articles to plausible barriers (privacy/regulation/integration); no direct empirical test in the review.
Widespread deployment of similar models could create correlated failures or fraud vectors, implying systemic risk that may warrant macroprudential attention.
Analytic caution based on model homogeneity and case/literature discussion; speculative systemic risk concern rather than empirically demonstrated.
There is regulatory uncertainty around AI-generated filings and responsibility/liability for automated outputs.
Analysis and literature review discuss unclear regulatory positions and legal risks noted in case organizations' deployment considerations.
Integration complexity with legacy ERP/financial systems and sharing-center processes is a significant implementation challenge.
Case study narratives describe integration work and friction points; analytic framing highlights ERP compatibility issues.
Model hallucinations, lack of explainability, and limited audit trails limit safe adoption.
Paper cites literature and case observations about model reliability and explainability issues; examples and discussion are qualitative.
Data privacy, confidentiality, and cross-border data transfer concerns are important barriers to deployment.
Challenges enumerated from case studies and literature; specific organizational concerns cited in cases (Xiaomi, Deloitte) and in regulatory discussion.
Explainability, auditability, or data-localization requirements could favor larger vendors with compliance capacity, increasing market concentration and affecting competition among AI suppliers.
Market-structure argument grounded in regulatory-compliance burden analysis and comparative examples; not supported by empirical market data in the study.
Legal uncertainty and strict procedural requirements increase compliance costs and regulatory risk, which can slow AI adoption by firms and public agencies.
Theoretical economic implications drawn from legal analysis and comparative observations; no empirical measurement of costs or adoption rates in the study.
AI can restrict or reshape human administrative discretion in legally sensitive ways.
Doctrinal analysis of statutory specificity and formal procedural requirements in civil-law contexts, illustrated with Vietnam as the exemplar case; comparative observations.
Capabilities and data advantages for certain vendors could lead to market concentration and platform dominance in AI-driven educational feedback.
Expert concern synthesized from the workshop of 50 scholars about market dynamics; theoretical warning without empirical market-structure analysis in the report.
Differential access to high-quality AI feedback systems and bias in training data can exacerbate educational inequalities and harm marginalized groups.
Expert consensus and thematic analysis from the 50-scholar workshop, raising equity and bias risks; no empirical subgroup effectiveness estimates included.
Learners may over-rely on AI feedback or game systems to obtain desirable responses, reducing effortful learning.
Workshop participant concerns synthesized qualitatively; cited as risk and an open empirical question—no experimental data provided.